One way to overcome this limitation is by transferring knowledge. There are several ways that knowledge can be transferred in dRL frameworks [1]}. In reward shaping, the acquired knowledge is utilized to alter the reward function of the target task in order to accelerate training [2]}. An issue here is related to changing the convergence of the policy by altering the reward function. [3]} show how any function can be used to reshape the reward function while maintaining policy invariance.
